Overview

Brought to you by YData

Dataset statistics

Number of variables28
Number of observations22568
Missing cells22568
Missing cells (%)3.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.0 MiB
Average record size in memory232.0 B

Variable types

Categorical11
DateTime1
TimeSeries15
Numeric1

Timeseries statistics

Number of series15
Time series length22568
Starting point2000-01-01 00:00:00
Ending point2015-12-31 00:00:00
Period6 hours, 12 minutes and 51.01 seconds
2025-09-23T15:58:40.339133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:41.513162image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Alerts

State Code has constant value "4"Constant
County Code has constant value "13"Constant
Site Num has constant value "3002"Constant
Address has constant value "1645 E ROOSEVELT ST-CENTRAL PHOENIX STN"Constant
State has constant value "Arizona"Constant
County has constant value "Maricopa"Constant
City has constant value "Phoenix"Constant
NO2 Units has constant value "Parts per billion"Constant
O3 Units has constant value "Parts per million"Constant
SO2 Units has constant value "Parts per billion"Constant
CO Units has constant value "Parts per million"Constant
CO 1st Max Value is highly overall correlated with CO AQI and 8 other fieldsHigh correlation
CO AQI is highly overall correlated with CO 1st Max Value and 10 other fieldsHigh correlation
CO Mean is highly overall correlated with CO 1st Max Value and 9 other fieldsHigh correlation
NO2 1st Max Value is highly overall correlated with CO 1st Max Value and 6 other fieldsHigh correlation
NO2 AQI is highly overall correlated with CO 1st Max Value and 6 other fieldsHigh correlation
NO2 Mean is highly overall correlated with CO 1st Max Value and 8 other fieldsHigh correlation
O3 1st Max Value is highly overall correlated with CO AQI and 2 other fieldsHigh correlation
O3 AQI is highly overall correlated with CO AQI and 3 other fieldsHigh correlation
O3 Mean is highly overall correlated with CO 1st Max Value and 7 other fieldsHigh correlation
SO2 1st Max Value is highly overall correlated with CO 1st Max Value and 8 other fieldsHigh correlation
SO2 AQI is highly overall correlated with CO 1st Max Value and 8 other fieldsHigh correlation
SO2 Mean is highly overall correlated with CO 1st Max Value and 5 other fieldsHigh correlation
SO2 AQI has 11281 (50.0%) missing valuesMissing
CO AQI has 11287 (50.0%) missing valuesMissing
NO2 Mean is non stationaryNon stationary
NO2 1st Max Value is non stationaryNon stationary
NO2 1st Max Hour is non stationaryNon stationary
NO2 AQI is non stationaryNon stationary
O3 Mean is non stationaryNon stationary
O3 1st Max Value is non stationaryNon stationary
O3 1st Max Hour is non stationaryNon stationary
O3 AQI is non stationaryNon stationary
SO2 Mean is non stationaryNon stationary
SO2 1st Max Value is non stationaryNon stationary
SO2 1st Max Hour is non stationaryNon stationary
SO2 AQI is non stationaryNon stationary
CO Mean is non stationaryNon stationary
CO 1st Max Value is non stationaryNon stationary
CO AQI is non stationaryNon stationary
NO2 Mean is seasonalSeasonal
NO2 1st Max Value is seasonalSeasonal
NO2 1st Max Hour is seasonalSeasonal
NO2 AQI is seasonalSeasonal
O3 Mean is seasonalSeasonal
O3 1st Max Value is seasonalSeasonal
O3 1st Max Hour is seasonalSeasonal
O3 AQI is seasonalSeasonal
SO2 Mean is seasonalSeasonal
SO2 1st Max Value is seasonalSeasonal
SO2 1st Max Hour is seasonalSeasonal
SO2 AQI is seasonalSeasonal
CO Mean is seasonalSeasonal
CO 1st Max Value is seasonalSeasonal
CO AQI is seasonalSeasonal
NO2 1st Max Hour has 3220 (14.3%) zerosZeros
SO2 Mean has 708 (3.1%) zerosZeros
SO2 1st Max Value has 708 (3.1%) zerosZeros
SO2 1st Max Hour has 2732 (12.1%) zerosZeros
SO2 AQI has 384 (1.7%) zerosZeros
CO 1st Max Hour has 4213 (18.7%) zerosZeros

Reproduction

Analysis started2025-09-23 15:57:35.456714
Analysis finished2025-09-23 15:58:39.426567
Duration1 minute and 3.97 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

State Code
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size352.6 KiB
4
22568 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters22568
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
422568
100.0%

Length

2025-09-23T15:58:41.928942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-23T15:58:41.967916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
422568
100.0%

Most occurring characters

ValueCountFrequency (%)
422568
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)22568
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
422568
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)22568
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
422568
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)22568
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
422568
100.0%

County Code
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size352.6 KiB
13
22568 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters45136
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row13
2nd row13
3rd row13
4th row13
5th row13

Common Values

ValueCountFrequency (%)
1322568
100.0%

Length

2025-09-23T15:58:42.013005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-23T15:58:42.050793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1322568
100.0%

Most occurring characters

ValueCountFrequency (%)
122568
50.0%
322568
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)45136
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
122568
50.0%
322568
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)45136
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
122568
50.0%
322568
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)45136
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
122568
50.0%
322568
50.0%

Site Num
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size352.6 KiB
3002
22568 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters90272
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3002
2nd row3002
3rd row3002
4th row3002
5th row3002

Common Values

ValueCountFrequency (%)
300222568
100.0%

Length

2025-09-23T15:58:42.100322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-23T15:58:42.138910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
300222568
100.0%

Most occurring characters

ValueCountFrequency (%)
045136
50.0%
322568
25.0%
222568
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)90272
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
045136
50.0%
322568
25.0%
222568
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)90272
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
045136
50.0%
322568
25.0%
222568
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)90272
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
045136
50.0%
322568
25.0%
222568
25.0%

Address
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size352.6 KiB
1645 E ROOSEVELT ST-CENTRAL PHOENIX STN
22568 

Length

Max length39
Median length39
Mean length39
Min length39

Characters and Unicode

Total characters880152
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1645 E ROOSEVELT ST-CENTRAL PHOENIX STN
2nd row1645 E ROOSEVELT ST-CENTRAL PHOENIX STN
3rd row1645 E ROOSEVELT ST-CENTRAL PHOENIX STN
4th row1645 E ROOSEVELT ST-CENTRAL PHOENIX STN
5th row1645 E ROOSEVELT ST-CENTRAL PHOENIX STN

Common Values

ValueCountFrequency (%)
1645 E ROOSEVELT ST-CENTRAL PHOENIX STN22568
100.0%

Length

2025-09-23T15:58:42.185218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-23T15:58:42.226936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
164522568
16.7%
e22568
16.7%
roosevelt22568
16.7%
st-central22568
16.7%
phoenix22568
16.7%
stn22568
16.7%

Most occurring characters

ValueCountFrequency (%)
E112840
12.8%
112840
12.8%
T90272
 
10.3%
O67704
 
7.7%
S67704
 
7.7%
N67704
 
7.7%
L45136
 
5.1%
R45136
 
5.1%
522568
 
2.6%
422568
 
2.6%
Other values (10)225680
25.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)880152
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E112840
12.8%
112840
12.8%
T90272
 
10.3%
O67704
 
7.7%
S67704
 
7.7%
N67704
 
7.7%
L45136
 
5.1%
R45136
 
5.1%
522568
 
2.6%
422568
 
2.6%
Other values (10)225680
25.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)880152
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E112840
12.8%
112840
12.8%
T90272
 
10.3%
O67704
 
7.7%
S67704
 
7.7%
N67704
 
7.7%
L45136
 
5.1%
R45136
 
5.1%
522568
 
2.6%
422568
 
2.6%
Other values (10)225680
25.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)880152
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E112840
12.8%
112840
12.8%
T90272
 
10.3%
O67704
 
7.7%
S67704
 
7.7%
N67704
 
7.7%
L45136
 
5.1%
R45136
 
5.1%
522568
 
2.6%
422568
 
2.6%
Other values (10)225680
25.6%

State
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size352.6 KiB
Arizona
22568 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters157976
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowArizona
2nd rowArizona
3rd rowArizona
4th rowArizona
5th rowArizona

Common Values

ValueCountFrequency (%)
Arizona22568
100.0%

Length

2025-09-23T15:58:42.275306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-23T15:58:42.313368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
arizona22568
100.0%

Most occurring characters

ValueCountFrequency (%)
A22568
14.3%
r22568
14.3%
i22568
14.3%
z22568
14.3%
o22568
14.3%
n22568
14.3%
a22568
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)157976
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A22568
14.3%
r22568
14.3%
i22568
14.3%
z22568
14.3%
o22568
14.3%
n22568
14.3%
a22568
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)157976
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A22568
14.3%
r22568
14.3%
i22568
14.3%
z22568
14.3%
o22568
14.3%
n22568
14.3%
a22568
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)157976
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A22568
14.3%
r22568
14.3%
i22568
14.3%
z22568
14.3%
o22568
14.3%
n22568
14.3%
a22568
14.3%

County
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size352.6 KiB
Maricopa
22568 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters180544
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMaricopa
2nd rowMaricopa
3rd rowMaricopa
4th rowMaricopa
5th rowMaricopa

Common Values

ValueCountFrequency (%)
Maricopa22568
100.0%

Length

2025-09-23T15:58:42.361404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-23T15:58:42.398804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
maricopa22568
100.0%

Most occurring characters

ValueCountFrequency (%)
a45136
25.0%
M22568
12.5%
r22568
12.5%
i22568
12.5%
c22568
12.5%
o22568
12.5%
p22568
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)180544
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a45136
25.0%
M22568
12.5%
r22568
12.5%
i22568
12.5%
c22568
12.5%
o22568
12.5%
p22568
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)180544
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a45136
25.0%
M22568
12.5%
r22568
12.5%
i22568
12.5%
c22568
12.5%
o22568
12.5%
p22568
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)180544
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a45136
25.0%
M22568
12.5%
r22568
12.5%
i22568
12.5%
c22568
12.5%
o22568
12.5%
p22568
12.5%

City
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size352.6 KiB
Phoenix
22568 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters157976
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPhoenix
2nd rowPhoenix
3rd rowPhoenix
4th rowPhoenix
5th rowPhoenix

Common Values

ValueCountFrequency (%)
Phoenix22568
100.0%

Length

2025-09-23T15:58:42.444676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-23T15:58:42.484428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
phoenix22568
100.0%

Most occurring characters

ValueCountFrequency (%)
P22568
14.3%
h22568
14.3%
o22568
14.3%
e22568
14.3%
n22568
14.3%
i22568
14.3%
x22568
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)157976
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P22568
14.3%
h22568
14.3%
o22568
14.3%
e22568
14.3%
n22568
14.3%
i22568
14.3%
x22568
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)157976
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P22568
14.3%
h22568
14.3%
o22568
14.3%
e22568
14.3%
n22568
14.3%
i22568
14.3%
x22568
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)157976
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P22568
14.3%
h22568
14.3%
o22568
14.3%
e22568
14.3%
n22568
14.3%
i22568
14.3%
x22568
14.3%
Distinct5646
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size352.6 KiB
Minimum2000-01-01 00:00:00
Maximum2015-12-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-09-23T15:58:42.544186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:42.640330image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

NO2 Units
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size352.6 KiB
Parts per billion
22568 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters383656
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per billion
2nd rowParts per billion
3rd rowParts per billion
4th rowParts per billion
5th rowParts per billion

Common Values

ValueCountFrequency (%)
Parts per billion22568
100.0%

Length

2025-09-23T15:58:42.718607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-23T15:58:42.758549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
parts22568
33.3%
per22568
33.3%
billion22568
33.3%

Most occurring characters

ValueCountFrequency (%)
r45136
11.8%
i45136
11.8%
l45136
11.8%
45136
11.8%
P22568
 
5.9%
s22568
 
5.9%
t22568
 
5.9%
a22568
 
5.9%
p22568
 
5.9%
b22568
 
5.9%
Other values (3)67704
17.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)383656
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r45136
11.8%
i45136
11.8%
l45136
11.8%
45136
11.8%
P22568
 
5.9%
s22568
 
5.9%
t22568
 
5.9%
a22568
 
5.9%
p22568
 
5.9%
b22568
 
5.9%
Other values (3)67704
17.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)383656
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r45136
11.8%
i45136
11.8%
l45136
11.8%
45136
11.8%
P22568
 
5.9%
s22568
 
5.9%
t22568
 
5.9%
a22568
 
5.9%
p22568
 
5.9%
b22568
 
5.9%
Other values (3)67704
17.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)383656
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r45136
11.8%
i45136
11.8%
l45136
11.8%
45136
11.8%
P22568
 
5.9%
s22568
 
5.9%
t22568
 
5.9%
a22568
 
5.9%
p22568
 
5.9%
b22568
 
5.9%
Other values (3)67704
17.6%

NO2 Mean
Numeric time series

High correlation  Non stationary  Seasonal 

Distinct2029
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.423615
Minimum0.52381
Maximum73.285714
Zeros0
Zeros (%)0.0%
Memory size352.6 KiB
2025-09-23T15:58:42.866793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.52381
5-th percentile8.041667
Q115.541667
median22.952381
Q330.521739
95-th percentile40.375
Maximum73.285714
Range72.761904
Interquartile range (IQR)14.980072

Descriptive statistics

Standard deviation10.140947
Coefficient of variation (CV)0.43293691
Kurtosis-0.2114701
Mean23.423615
Median Absolute Deviation (MAD)7.494048
Skewness0.30076624
Sum528624.14
Variance102.83882
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value4.613554701 × 10-13
2025-09-23T15:58:42.986291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2025-09-23T15:58:43.350235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps29
min3 days
max8 weeks and 6 days
mean1 week, 8 hours and 16 minutes
std1 week, 3 days and 19 hours
2025-09-23T15:58:43.493459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
16.12556
 
0.2%
15.58333352
 
0.2%
20.87552
 
0.2%
24.08333352
 
0.2%
22.16666748
 
0.2%
15.16666748
 
0.2%
21.2548
 
0.2%
1748
 
0.2%
24.2548
 
0.2%
27.54166744
 
0.2%
Other values (2019)22072
97.8%
ValueCountFrequency (%)
0.523814
< 0.1%
0.88
< 0.1%
0.8421054
< 0.1%
1.1818184
< 0.1%
1.2708334
< 0.1%
1.3833334
< 0.1%
1.4583334
< 0.1%
1.5541674
< 0.1%
1.7041674
< 0.1%
1.8541674
< 0.1%
ValueCountFrequency (%)
73.2857144
< 0.1%
67.0909094
< 0.1%
66.7916674
< 0.1%
66.5416674
< 0.1%
59.254
< 0.1%
59.0416674
< 0.1%
584
< 0.1%
57.8636364
< 0.1%
57.8333334
< 0.1%
56.0454554
< 0.1%
2025-09-23T15:58:43.136461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

NO2 1st Max Value
Numeric time series

High correlation  Non stationary  Seasonal 

Distinct119
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.745108
Minimum1.9
Maximum124
Zeros0
Zeros (%)0.0%
Memory size352.6 KiB
2025-09-23T15:58:43.844098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.9
5-th percentile20
Q136
median45
Q354
95-th percentile67
Maximum124
Range122.1
Interquartile range (IQR)18

Descriptive statistics

Standard deviation14.181022
Coefficient of variation (CV)0.31692899
Kurtosis0.46735049
Mean44.745108
Median Absolute Deviation (MAD)9
Skewness-0.081769914
Sum1009807.6
Variance201.10139
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value4.501673973 × 10-19
2025-09-23T15:58:43.957904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2025-09-23T15:58:44.318355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps29
min3 days
max8 weeks and 6 days
mean1 week, 8 hours and 16 minutes
std1 week, 3 days and 19 hours
2025-09-23T15:58:44.461397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
47720
 
3.2%
45720
 
3.2%
51704
 
3.1%
50678
 
3.0%
44676
 
3.0%
46676
 
3.0%
48664
 
2.9%
43644
 
2.9%
54642
 
2.8%
49640
 
2.8%
Other values (109)15804
70.0%
ValueCountFrequency (%)
1.94
< 0.1%
24
< 0.1%
34
< 0.1%
3.24
< 0.1%
3.44
< 0.1%
3.74
< 0.1%
3.84
< 0.1%
48
< 0.1%
4.34
< 0.1%
4.64
< 0.1%
ValueCountFrequency (%)
1244
< 0.1%
1174
< 0.1%
1164
< 0.1%
1064
< 0.1%
1018
< 0.1%
954
< 0.1%
944
< 0.1%
924
< 0.1%
894
< 0.1%
884
< 0.1%
2025-09-23T15:58:44.106689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

NO2 1st Max Hour
Numeric time series

Non stationary  Seasonal  Zeros 

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.268256
Minimum0
Maximum23
Zeros3220
Zeros (%)14.3%
Memory size352.6 KiB
2025-09-23T15:58:44.641911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median19
Q321
95-th percentile23
Maximum23
Range23
Interquartile range (IQR)16

Descriptive statistics

Standard deviation8.7551338
Coefficient of variation (CV)0.65985566
Kurtosis-1.5848044
Mean13.268256
Median Absolute Deviation (MAD)3
Skewness-0.4069905
Sum299438
Variance76.652368
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.616565061 × 10-28
2025-09-23T15:58:44.758945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
2025-09-23T15:58:45.106725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps29
min3 days
max8 weeks and 6 days
mean1 week, 8 hours and 16 minutes
std1 week, 3 days and 19 hours
2025-09-23T15:58:45.282537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
03220
14.3%
203168
14.0%
212854
12.6%
222128
9.4%
192108
9.3%
231516
6.7%
61374
6.1%
51252
 
5.5%
18944
 
4.2%
1792
 
3.5%
Other values (14)3212
14.2%
ValueCountFrequency (%)
03220
14.3%
1792
 
3.5%
2368
 
1.6%
3328
 
1.5%
4484
 
2.1%
51252
 
5.5%
61374
6.1%
7724
 
3.2%
8500
 
2.2%
9316
 
1.4%
ValueCountFrequency (%)
231516
6.7%
222128
9.4%
212854
12.6%
203168
14.0%
192108
9.3%
18944
 
4.2%
17140
 
0.6%
1636
 
0.2%
1528
 
0.1%
1416
 
0.1%
2025-09-23T15:58:44.897965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

NO2 AQI
Numeric time series

High correlation  Non stationary  Seasonal 

Distinct93
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.433357
Minimum1
Maximum105
Zeros0
Zeros (%)0.0%
Memory size352.6 KiB
2025-09-23T15:58:45.633172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile19
Q134
median42
Q351
95-th percentile65
Maximum105
Range104
Interquartile range (IQR)17

Descriptive statistics

Standard deviation13.760116
Coefficient of variation (CV)0.32427593
Kurtosis0.44346329
Mean42.433357
Median Absolute Deviation (MAD)9
Skewness0.013623845
Sum957636
Variance189.3408
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value4.052713506 × 10-19
2025-09-23T15:58:45.744098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2025-09-23T15:58:46.099598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps29
min3 days
max8 weeks and 6 days
mean1 week, 8 hours and 16 minutes
std1 week, 3 days and 19 hours
2025-09-23T15:58:46.240166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
421396
 
6.2%
44720
 
3.2%
48704
 
3.1%
47678
 
3.0%
43676
 
3.0%
45664
 
2.9%
41644
 
2.9%
51642
 
2.8%
46640
 
2.8%
39612
 
2.7%
Other values (83)15192
67.3%
ValueCountFrequency (%)
14
 
< 0.1%
24
 
< 0.1%
320
 
0.1%
424
 
0.1%
528
0.1%
68
 
< 0.1%
724
 
0.1%
848
0.2%
940
0.2%
1060
0.3%
ValueCountFrequency (%)
1054
 
< 0.1%
1048
< 0.1%
1024
 
< 0.1%
1018
< 0.1%
954
 
< 0.1%
944
 
< 0.1%
914
 
< 0.1%
884
 
< 0.1%
874
 
< 0.1%
8612
0.1%
2025-09-23T15:58:45.888496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

O3 Units
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size352.6 KiB
Parts per million
22568 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters383656
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per million
2nd rowParts per million
3rd rowParts per million
4th rowParts per million
5th rowParts per million

Common Values

ValueCountFrequency (%)
Parts per million22568
100.0%

Length

2025-09-23T15:58:46.362404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-23T15:58:46.400971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
parts22568
33.3%
per22568
33.3%
million22568
33.3%

Most occurring characters

ValueCountFrequency (%)
r45136
11.8%
i45136
11.8%
l45136
11.8%
45136
11.8%
P22568
 
5.9%
s22568
 
5.9%
t22568
 
5.9%
a22568
 
5.9%
p22568
 
5.9%
m22568
 
5.9%
Other values (3)67704
17.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)383656
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r45136
11.8%
i45136
11.8%
l45136
11.8%
45136
11.8%
P22568
 
5.9%
s22568
 
5.9%
t22568
 
5.9%
a22568
 
5.9%
p22568
 
5.9%
m22568
 
5.9%
Other values (3)67704
17.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)383656
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r45136
11.8%
i45136
11.8%
l45136
11.8%
45136
11.8%
P22568
 
5.9%
s22568
 
5.9%
t22568
 
5.9%
a22568
 
5.9%
p22568
 
5.9%
m22568
 
5.9%
Other values (3)67704
17.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)383656
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r45136
11.8%
i45136
11.8%
l45136
11.8%
45136
11.8%
P22568
 
5.9%
s22568
 
5.9%
t22568
 
5.9%
a22568
 
5.9%
p22568
 
5.9%
m22568
 
5.9%
Other values (3)67704
17.6%

O3 Mean
Numeric time series

High correlation  Non stationary  Seasonal 

Distinct1238
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.023885065
Minimum0.001
Maximum0.063167
Zeros0
Zeros (%)0.0%
Memory size352.6 KiB
2025-09-23T15:58:46.507923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.006875
Q10.014625
median0.023542
Q30.032208
95-th percentile0.043125
Maximum0.063167
Range0.062167
Interquartile range (IQR)0.017583

Descriptive statistics

Standard deviation0.011318776
Coefficient of variation (CV)0.47388511
Kurtosis-0.66123486
Mean0.023885065
Median Absolute Deviation (MAD)0.008833
Skewness0.24381558
Sum539.03814
Variance0.0001281147
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.201841185 × 10-7
2025-09-23T15:58:46.621929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2025-09-23T15:58:47.114862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps29
min3 days
max8 weeks and 6 days
mean1 week, 8 hours and 16 minutes
std1 week, 3 days and 19 hours
2025-09-23T15:58:47.256313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.02937576
 
0.3%
0.01462560
 
0.3%
0.01545860
 
0.3%
0.02495856
 
0.2%
0.02354256
 
0.2%
0.02862556
 
0.2%
0.02933352
 
0.2%
0.01166752
 
0.2%
0.02562552
 
0.2%
0.01952
 
0.2%
Other values (1228)21996
97.5%
ValueCountFrequency (%)
0.0018
< 0.1%
0.0015454
< 0.1%
0.0016114
< 0.1%
0.0016678
< 0.1%
0.001754
< 0.1%
0.0018334
< 0.1%
0.0019174
< 0.1%
0.0028
< 0.1%
0.0020424
< 0.1%
0.0023334
< 0.1%
ValueCountFrequency (%)
0.0631674
< 0.1%
0.0583334
< 0.1%
0.0566254
< 0.1%
0.0563754
< 0.1%
0.0563334
< 0.1%
0.0558334
< 0.1%
0.0557084
< 0.1%
0.0550834
< 0.1%
0.0550424
< 0.1%
0.0554
< 0.1%
2025-09-23T15:58:46.772523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

O3 1st Max Value
Numeric time series

High correlation  Non stationary  Seasonal 

Distinct86
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.043149061
Minimum0.001
Maximum0.089
Zeros0
Zeros (%)0.0%
Memory size352.6 KiB
2025-09-23T15:58:47.438647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.016
Q10.033
median0.044
Q30.054
95-th percentile0.066
Maximum0.089
Range0.088
Interquartile range (IQR)0.021

Descriptive statistics

Standard deviation0.015281374
Coefficient of variation (CV)0.35415311
Kurtosis-0.43241989
Mean0.043149061
Median Absolute Deviation (MAD)0.011
Skewness-0.22490322
Sum973.788
Variance0.00023352039
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.273232853 × 10-7
2025-09-23T15:58:47.551379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2025-09-23T15:58:48.025428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps29
min3 days
max8 weeks and 6 days
mean1 week, 8 hours and 16 minutes
std1 week, 3 days and 19 hours
2025-09-23T15:58:48.166017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.048630
 
2.8%
0.054592
 
2.6%
0.044588
 
2.6%
0.047584
 
2.6%
0.053580
 
2.6%
0.04576
 
2.6%
0.051576
 
2.6%
0.05576
 
2.6%
0.046568
 
2.5%
0.049568
 
2.5%
Other values (76)16730
74.1%
ValueCountFrequency (%)
0.0018
 
< 0.1%
0.0024
 
< 0.1%
0.00336
0.2%
0.00416
 
0.1%
0.00532
 
0.1%
0.00644
0.2%
0.00748
0.2%
0.00884
0.4%
0.00980
0.4%
0.0188
0.4%
ValueCountFrequency (%)
0.0894
 
< 0.1%
0.0884
 
< 0.1%
0.0848
 
< 0.1%
0.0838
 
< 0.1%
0.0828
 
< 0.1%
0.08120
0.1%
0.0812
 
0.1%
0.07920
0.1%
0.07828
0.1%
0.07736
0.2%
2025-09-23T15:58:47.701504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

O3 1st Max Hour
Numeric time series

Non stationary  Seasonal 

Distinct22
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.30911
Minimum0
Maximum23
Zeros128
Zeros (%)0.6%
Memory size352.6 KiB
2025-09-23T15:58:48.349615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q110
median10
Q311
95-th percentile12
Maximum23
Range23
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.8934208
Coefficient of variation (CV)0.18366481
Kurtosis20.390093
Mean10.30911
Median Absolute Deviation (MAD)1
Skewness1.9691513
Sum232656
Variance3.5850422
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.405028932 × 10-29
2025-09-23T15:58:48.441247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
2025-09-23T15:58:48.895498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps29
min3 days
max8 weeks and 6 days
mean1 week, 8 hours and 16 minutes
std1 week, 3 days and 19 hours
2025-09-23T15:58:49.032342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
109450
41.9%
116420
28.4%
93972
17.6%
121018
 
4.5%
8612
 
2.7%
13236
 
1.0%
0128
 
0.6%
14116
 
0.5%
7104
 
0.5%
2380
 
0.4%
Other values (12)432
 
1.9%
ValueCountFrequency (%)
0128
 
0.6%
112
 
0.1%
316
 
0.1%
54
 
< 0.1%
620
 
0.1%
7104
 
0.5%
8612
 
2.7%
93972
17.6%
109450
41.9%
116420
28.4%
ValueCountFrequency (%)
2380
0.4%
2260
0.3%
2160
0.3%
2068
0.3%
1952
0.2%
1836
 
0.2%
1720
 
0.1%
1640
 
0.2%
1544
 
0.2%
14116
0.5%
2025-09-23T15:58:48.576462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

O3 AQI
Numeric time series

High correlation  Non stationary  Seasonal 

Distinct82
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.556806
Minimum1
Maximum135
Zeros0
Zeros (%)0.0%
Memory size352.6 KiB
2025-09-23T15:58:49.214419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile14
Q128
median38
Q347
95-th percentile77
Maximum135
Range134
Interquartile range (IQR)19

Descriptive statistics

Standard deviation18.19625
Coefficient of variation (CV)0.46000302
Kurtosis2.4901098
Mean39.556806
Median Absolute Deviation (MAD)9
Skewness1.1400679
Sum892718
Variance331.10352
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value3.060826789 × 10-11
2025-09-23T15:58:49.329722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2025-09-23T15:58:49.806212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps29
min3 days
max8 weeks and 6 days
mean1 week, 8 hours and 16 minutes
std1 week, 3 days and 19 hours
2025-09-23T15:58:49.946018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
421092
 
4.8%
47934
 
4.1%
36908
 
4.0%
31854
 
3.8%
44686
 
3.0%
41626
 
2.8%
37604
 
2.7%
46596
 
2.6%
25596
 
2.6%
40576
 
2.6%
Other values (72)15096
66.9%
ValueCountFrequency (%)
18
 
< 0.1%
24
 
< 0.1%
352
 
0.2%
432
 
0.1%
544
 
0.2%
648
 
0.2%
784
0.4%
8156
0.7%
9100
0.4%
1092
0.4%
ValueCountFrequency (%)
1354
 
< 0.1%
1324
 
< 0.1%
1298
 
< 0.1%
12216
0.1%
1198
 
< 0.1%
1168
 
< 0.1%
11512
0.1%
11420
0.1%
1124
 
< 0.1%
11112
0.1%
2025-09-23T15:58:49.592266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

SO2 Units
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size352.6 KiB
Parts per billion
22568 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters383656
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per billion
2nd rowParts per billion
3rd rowParts per billion
4th rowParts per billion
5th rowParts per billion

Common Values

ValueCountFrequency (%)
Parts per billion22568
100.0%

Length

2025-09-23T15:58:50.072815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-23T15:58:50.112009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
parts22568
33.3%
per22568
33.3%
billion22568
33.3%

Most occurring characters

ValueCountFrequency (%)
r45136
11.8%
i45136
11.8%
l45136
11.8%
45136
11.8%
P22568
 
5.9%
s22568
 
5.9%
t22568
 
5.9%
a22568
 
5.9%
p22568
 
5.9%
b22568
 
5.9%
Other values (3)67704
17.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)383656
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r45136
11.8%
i45136
11.8%
l45136
11.8%
45136
11.8%
P22568
 
5.9%
s22568
 
5.9%
t22568
 
5.9%
a22568
 
5.9%
p22568
 
5.9%
b22568
 
5.9%
Other values (3)67704
17.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)383656
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r45136
11.8%
i45136
11.8%
l45136
11.8%
45136
11.8%
P22568
 
5.9%
s22568
 
5.9%
t22568
 
5.9%
a22568
 
5.9%
p22568
 
5.9%
b22568
 
5.9%
Other values (3)67704
17.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)383656
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r45136
11.8%
i45136
11.8%
l45136
11.8%
45136
11.8%
P22568
 
5.9%
s22568
 
5.9%
t22568
 
5.9%
a22568
 
5.9%
p22568
 
5.9%
b22568
 
5.9%
Other values (3)67704
17.6%

SO2 Mean
Numeric time series

High correlation  Non stationary  Seasonal  Zeros 

Distinct1317
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7070794
Minimum0
Maximum12.166667
Zeros708
Zeros (%)3.1%
Memory size352.6 KiB
2025-09-23T15:58:50.217895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.075
Q10.875
median1.325
Q32.25
95-th percentile4.391304
Maximum12.166667
Range12.166667
Interquartile range (IQR)1.375

Descriptive statistics

Standard deviation1.4038768
Coefficient of variation (CV)0.82238519
Kurtosis6.3707314
Mean1.7070794
Median Absolute Deviation (MAD)0.675
Skewness1.9544034
Sum38525.368
Variance1.9708702
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value8.147275733 × 10-18
2025-09-23T15:58:50.448604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2025-09-23T15:58:50.811582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps29
min3 days
max8 weeks and 6 days
mean1 week, 8 hours and 16 minutes
std1 week, 3 days and 19 hours
2025-09-23T15:58:50.951069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
11054
 
4.7%
0708
 
3.1%
1.083333262
 
1.2%
1.125236
 
1.0%
1.25222
 
1.0%
2222
 
1.0%
1.041667214
 
0.9%
1.075198
 
0.9%
1.5196
 
0.9%
1.166667178
 
0.8%
Other values (1307)19078
84.5%
ValueCountFrequency (%)
0708
3.1%
0.0375110
 
0.5%
0.041667110
 
0.5%
0.04285738
 
0.2%
0.04347824
 
0.1%
0.04545510
 
< 0.1%
0.0476194
 
< 0.1%
0.0514
 
0.1%
0.0526322
 
< 0.1%
0.0555562
 
< 0.1%
ValueCountFrequency (%)
12.1666672
< 0.1%
12.1252
< 0.1%
12.0833332
< 0.1%
12.052
< 0.1%
11.7666672
< 0.1%
11.6254
< 0.1%
11.62
< 0.1%
11.58752
< 0.1%
10.9523812
< 0.1%
10.9166672
< 0.1%
2025-09-23T15:58:50.596957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

SO2 1st Max Value
Numeric time series

High correlation  Non stationary  Seasonal  Zeros 

Distinct87
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6576391
Minimum0
Maximum69
Zeros708
Zeros (%)3.1%
Memory size352.6 KiB
2025-09-23T15:58:51.131238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.6
Q11.6
median3
Q35
95-th percentile10
Maximum69
Range69
Interquartile range (IQR)3.4

Descriptive statistics

Standard deviation3.1020485
Coefficient of variation (CV)0.8481013
Kurtosis24.467107
Mean3.6576391
Median Absolute Deviation (MAD)1.6
Skewness2.7722686
Sum82545.6
Variance9.622705
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.796441733 × 10-16
2025-09-23T15:58:51.378194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2025-09-23T15:58:51.742996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps29
min3 days
max8 weeks and 6 days
mean1 week, 8 hours and 16 minutes
std1 week, 3 days and 19 hours
2025-09-23T15:58:51.882639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
23443
15.3%
12904
12.9%
32408
 
10.7%
41824
 
8.1%
51284
 
5.7%
6929
 
4.1%
1.3880
 
3.9%
1.6850
 
3.8%
2.3717
 
3.2%
0708
 
3.1%
Other values (77)6621
29.3%
ValueCountFrequency (%)
0708
3.1%
0.12
 
< 0.1%
0.24
 
< 0.1%
0.3266
 
1.2%
0.412
 
0.1%
0.512
 
0.1%
0.6250
 
1.1%
0.74
 
< 0.1%
0.812
 
0.1%
0.910
 
< 0.1%
ValueCountFrequency (%)
692
 
< 0.1%
422
 
< 0.1%
292
 
< 0.1%
282
 
< 0.1%
262
 
< 0.1%
25.62
 
< 0.1%
24.32
 
< 0.1%
246
< 0.1%
234
< 0.1%
22.62
 
< 0.1%
2025-09-23T15:58:51.529714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

SO2 1st Max Hour
Numeric time series

Non stationary  Seasonal  Zeros 

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.217742
Minimum0
Maximum23
Zeros2732
Zeros (%)12.1%
Memory size352.6 KiB
2025-09-23T15:58:52.062631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median8
Q320
95-th percentile23
Maximum23
Range23
Interquartile range (IQR)18

Descriptive statistics

Standard deviation8.4062502
Coefficient of variation (CV)0.82271115
Kurtosis-1.3038806
Mean10.217742
Median Absolute Deviation (MAD)6
Skewness0.46187117
Sum230594
Variance70.665043
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value9.647924471 × 10-29
2025-09-23T15:58:52.300287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
2025-09-23T15:58:52.644840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps29
min3 days
max8 weeks and 6 days
mean1 week, 8 hours and 16 minutes
std1 week, 3 days and 19 hours
2025-09-23T15:58:52.833638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
233919
17.4%
83056
13.5%
23002
13.3%
02732
12.1%
51682
7.5%
111373
 
6.1%
22936
 
4.1%
7842
 
3.7%
20842
 
3.7%
6820
 
3.6%
Other values (14)3364
14.9%
ValueCountFrequency (%)
02732
12.1%
1426
 
1.9%
23002
13.3%
3230
 
1.0%
4272
 
1.2%
51682
7.5%
6820
 
3.6%
7842
 
3.7%
83056
13.5%
9581
 
2.6%
ValueCountFrequency (%)
233919
17.4%
22936
 
4.1%
21757
 
3.4%
20842
 
3.7%
19150
 
0.7%
1824
 
0.1%
17102
 
0.5%
1616
 
0.1%
1518
 
0.1%
14275
 
1.2%
2025-09-23T15:58:52.436222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

SO2 AQI
Numeric time series

High correlation  Missing  Non stationary  Seasonal  Zeros 

Distinct30
Distinct (%)0.3%
Missing11281
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean5.8228936
Minimum0
Maximum92
Zeros384
Zeros (%)1.7%
Memory size352.6 KiB
2025-09-23T15:58:53.042002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q37
95-th percentile16
Maximum92
Range92
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.9061452
Coefficient of variation (CV)0.84256137
Kurtosis22.826395
Mean5.8228936
Median Absolute Deviation (MAD)3
Skewness2.695429
Sum65723
Variance24.070261
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value6.377439342 × 10-12
2025-09-23T15:58:53.135100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
2025-09-23T15:58:53.569011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps29
min3 days
max8 weeks and 6 days
mean1 week, 8 hours and 16 minutes
std1 week, 3 days and 19 hours
2025-09-23T15:58:53.692079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
32380
 
10.5%
41794
 
7.9%
11614
 
7.2%
61362
 
6.0%
71012
 
4.5%
9731
 
3.2%
10512
 
2.3%
11402
 
1.8%
0384
 
1.7%
13302
 
1.3%
Other values (20)794
 
3.5%
(Missing)11281
50.0%
ValueCountFrequency (%)
0384
 
1.7%
11614
7.2%
32380
10.5%
41794
7.9%
61362
6.0%
71012
4.5%
9731
 
3.2%
10512
 
2.3%
11402
 
1.8%
13302
 
1.3%
ValueCountFrequency (%)
922
 
< 0.1%
592
 
< 0.1%
412
 
< 0.1%
402
 
< 0.1%
372
 
< 0.1%
346
< 0.1%
334
< 0.1%
316
< 0.1%
308
< 0.1%
292
 
< 0.1%
2025-09-23T15:58:53.243840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

CO Units
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size352.6 KiB
Parts per million
22568 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters383656
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per million
2nd rowParts per million
3rd rowParts per million
4th rowParts per million
5th rowParts per million

Common Values

ValueCountFrequency (%)
Parts per million22568
100.0%

Length

2025-09-23T15:58:53.807660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-23T15:58:53.846759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
parts22568
33.3%
per22568
33.3%
million22568
33.3%

Most occurring characters

ValueCountFrequency (%)
r45136
11.8%
i45136
11.8%
l45136
11.8%
45136
11.8%
P22568
 
5.9%
s22568
 
5.9%
t22568
 
5.9%
a22568
 
5.9%
p22568
 
5.9%
m22568
 
5.9%
Other values (3)67704
17.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)383656
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r45136
11.8%
i45136
11.8%
l45136
11.8%
45136
11.8%
P22568
 
5.9%
s22568
 
5.9%
t22568
 
5.9%
a22568
 
5.9%
p22568
 
5.9%
m22568
 
5.9%
Other values (3)67704
17.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)383656
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r45136
11.8%
i45136
11.8%
l45136
11.8%
45136
11.8%
P22568
 
5.9%
s22568
 
5.9%
t22568
 
5.9%
a22568
 
5.9%
p22568
 
5.9%
m22568
 
5.9%
Other values (3)67704
17.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)383656
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r45136
11.8%
i45136
11.8%
l45136
11.8%
45136
11.8%
P22568
 
5.9%
s22568
 
5.9%
t22568
 
5.9%
a22568
 
5.9%
p22568
 
5.9%
m22568
 
5.9%
Other values (3)67704
17.6%

CO Mean
Numeric time series

High correlation  Non stationary  Seasonal 

Distinct1202
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5812409
Minimum0
Maximum3.575
Zeros28
Zeros (%)0.1%
Memory size352.6 KiB
2025-09-23T15:58:53.956282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.145833
Q10.2875
median0.454167
Q30.75
95-th percentile1.475
Maximum3.575
Range3.575
Interquartile range (IQR)0.4625

Descriptive statistics

Standard deviation0.4256152
Coefficient of variation (CV)0.73225267
Kurtosis3.6557701
Mean0.5812409
Median Absolute Deviation (MAD)0.204167
Skewness1.7096316
Sum13117.445
Variance0.1811483
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.097256943 × 10-10
2025-09-23T15:58:54.075438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2025-09-23T15:58:54.445252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps29
min3 days
max8 weeks and 6 days
mean1 week, 8 hours and 16 minutes
std1 week, 3 days and 19 hours
2025-09-23T15:58:54.707110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.3192
 
0.9%
0.25178
 
0.8%
0.329167174
 
0.8%
0.2625172
 
0.8%
0.2168
 
0.7%
0.316667168
 
0.7%
0.233333160
 
0.7%
0.270833160
 
0.7%
0.308333156
 
0.7%
0.320833154
 
0.7%
Other values (1192)20886
92.5%
ValueCountFrequency (%)
028
0.1%
0.0041676
 
< 0.1%
0.0083338
 
< 0.1%
0.01252
 
< 0.1%
0.01666722
0.1%
0.0181822
 
< 0.1%
0.022
 
< 0.1%
0.02083312
0.1%
0.0214292
 
< 0.1%
0.0217392
 
< 0.1%
ValueCountFrequency (%)
3.5752
< 0.1%
3.1363642
< 0.1%
3.01252
< 0.1%
2.9916672
< 0.1%
2.9583332
< 0.1%
2.8916672
< 0.1%
2.88752
< 0.1%
2.8130432
< 0.1%
2.78752
< 0.1%
2.7708332
< 0.1%
2025-09-23T15:58:54.227629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

CO 1st Max Value
Numeric time series

High correlation  Non stationary  Seasonal 

Distinct72
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2530397
Minimum0
Maximum8.1
Zeros28
Zeros (%)0.1%
Memory size352.6 KiB
2025-09-23T15:58:54.883100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3
Q10.5
median1
Q31.7
95-th percentile3.3
Maximum8.1
Range8.1
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation1.0102907
Coefficient of variation (CV)0.80627192
Kurtosis3.6041203
Mean1.2530397
Median Absolute Deviation (MAD)0.5
Skewness1.6875976
Sum28278.6
Variance1.0206874
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.609302893 × 10-10
2025-09-23T15:58:55.006213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2025-09-23T15:58:55.373644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps29
min3 days
max8 weeks and 6 days
mean1 week, 8 hours and 16 minutes
std1 week, 3 days and 19 hours
2025-09-23T15:58:55.641339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.41896
 
8.4%
0.51704
 
7.6%
0.31636
 
7.2%
0.61476
 
6.5%
0.71286
 
5.7%
0.81184
 
5.2%
0.91014
 
4.5%
11000
 
4.4%
1.1992
 
4.4%
1.2864
 
3.8%
Other values (62)9516
42.2%
ValueCountFrequency (%)
028
 
0.1%
0.1196
 
0.9%
0.2794
3.5%
0.31636
7.2%
0.41896
8.4%
0.51704
7.6%
0.61476
6.5%
0.71286
5.7%
0.81184
5.2%
0.91014
4.5%
ValueCountFrequency (%)
8.12
 
< 0.1%
82
 
< 0.1%
7.62
 
< 0.1%
7.32
 
< 0.1%
7.24
< 0.1%
72
 
< 0.1%
6.96
< 0.1%
6.62
 
< 0.1%
6.52
 
< 0.1%
6.46
< 0.1%
2025-09-23T15:58:55.159239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

CO 1st Max Hour
Real number (ℝ)

Zeros 

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.6669178
Minimum0
Maximum23
Zeros4213
Zeros (%)18.7%
Negative0
Negative (%)0.0%
Memory size352.6 KiB
2025-09-23T15:58:55.769925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median6
Q321
95-th percentile23
Maximum23
Range23
Interquartile range (IQR)20

Descriptive statistics

Standard deviation9.0495581
Coefficient of variation (CV)0.93613687
Kurtosis-1.4761671
Mean9.6669178
Median Absolute Deviation (MAD)6
Skewness0.47222081
Sum218163
Variance81.894502
MonotonicityNot monotonic
2025-09-23T15:58:55.838389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
04213
18.7%
233364
14.9%
11808
8.0%
61753
7.8%
221609
 
7.1%
51412
 
6.3%
71343
 
6.0%
21294
 
5.7%
211280
 
5.7%
8967
 
4.3%
Other values (14)3525
15.6%
ValueCountFrequency (%)
04213
18.7%
11808
8.0%
21294
 
5.7%
3712
 
3.2%
4500
 
2.2%
51412
 
6.3%
61753
7.8%
71343
 
6.0%
8967
 
4.3%
9400
 
1.8%
ValueCountFrequency (%)
233364
14.9%
221609
7.1%
211280
 
5.7%
20733
 
3.2%
19324
 
1.4%
1886
 
0.4%
1760
 
0.3%
1642
 
0.2%
1542
 
0.2%
1446
 
0.2%

CO AQI
Numeric time series

High correlation  Missing  Non stationary  Seasonal 

Distinct52
Distinct (%)0.5%
Missing11287
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean11.509618
Minimum0
Maximum59
Zeros16
Zeros (%)0.1%
Memory size352.6 KiB
2025-09-23T15:58:55.954107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q15
median9
Q315
95-th percentile30
Maximum59
Range59
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.8426413
Coefficient of variation (CV)0.76828278
Kurtosis2.8454352
Mean11.509618
Median Absolute Deviation (MAD)4
Skewness1.5586653
Sum129840
Variance78.192306
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value3.281995571 × 10-8
2025-09-23T15:58:56.070459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2025-09-23T15:58:56.394871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps29
min3 days
max8 weeks and 6 days
mean1 week, 11 hours and 35 minutes
std1 week, 3 days and 18 hours
2025-09-23T15:58:56.524024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
51146
 
5.1%
31102
 
4.9%
6972
 
4.3%
7790
 
3.5%
8708
 
3.1%
9684
 
3.0%
2559
 
2.5%
10534
 
2.4%
13528
 
2.3%
11492
 
2.2%
Other values (42)3766
 
16.7%
(Missing)11287
50.0%
ValueCountFrequency (%)
016
 
0.1%
1156
 
0.7%
2559
2.5%
31102
4.9%
51146
5.1%
6972
4.3%
7790
3.5%
8708
3.1%
9684
3.0%
10534
2.4%
ValueCountFrequency (%)
592
 
< 0.1%
572
 
< 0.1%
566
< 0.1%
554
 
< 0.1%
544
 
< 0.1%
526
< 0.1%
516
< 0.1%
502
 
< 0.1%
494
 
< 0.1%
4810
< 0.1%
2025-09-23T15:58:56.191011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

Interactions

2025-09-23T15:58:37.874064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:20.739291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:21.857769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:23.092096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:24.125319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:25.177903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:26.429846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:27.496835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:28.541334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:29.794331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:30.881107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:32.134473image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:33.175658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:34.202419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:35.496580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:36.654020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:37.944394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:20.810353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:21.929456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:23.159168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:24.191923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:25.252156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:26.499081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:27.563252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:28.611870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:29.864019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:30.953984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:32.203248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:33.243714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:34.276521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:35.571379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:36.723318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:38.015251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:20.880378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:21.996348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:23.225984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:24.259361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:25.321903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:26.567878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:27.630080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:28.822303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:29.933438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:31.024691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:32.269750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:33.307943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:34.348077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:35.646711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:36.790007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:38.080721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:20.947213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:22.062757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:23.286157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:24.321271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:25.387384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:26.631507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:27.692857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:28.888708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:29.997629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:31.094248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:32.332260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:33.370354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:34.415921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:35.714231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:36.853853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:38.147628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:21.015105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:22.267502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:23.350904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:24.385371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:25.594100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:26.699570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:27.758918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:28.955749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:30.065774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:31.163683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:32.396057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:33.432576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:34.484215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:35.785815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:36.917807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:38.218689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:21.088023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:22.338198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:23.417017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:24.451909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:25.663994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:26.766194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:27.825206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:29.026119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:30.134877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:31.236201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:32.465038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:33.499131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:34.702792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:35.858359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:36.988209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:38.283983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:21.155453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:22.403944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:23.481652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:24.517163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:25.731173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:26.828347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:27.887375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:29.094559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:30.201973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:31.304179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:32.528903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:33.560283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:34.772063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:35.926400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:37.053692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:38.349847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:21.224175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:22.469824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:23.541540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:24.579090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:25.798164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:26.894807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:27.949218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:29.161139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:30.266925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:31.372311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:32.591849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:33.620311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:34.842198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:35.996765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:37.115113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:38.418839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:21.295716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:22.539361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:23.607621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:24.646361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:25.869267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:26.963845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:28.016122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:29.232844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:30.339302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:31.444602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:32.657926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:33.686590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:34.916792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:36.072852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:37.183924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:38.488953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:21.366291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:22.608517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:23.673549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:24.713270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:25.939509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:27.031070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:28.083995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:29.302538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:30.405123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:31.641455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:32.722598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:33.749726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:34.989314image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:36.147306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:37.250667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:38.559150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:21.437874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:22.679669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:23.740525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:24.782344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:26.010997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:27.099340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:28.153035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:29.377499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:30.477927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:31.712648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:32.791096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:33.817061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:35.065529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:36.223816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:37.322118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:38.624377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:21.505429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:22.745722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:23.802120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:24.845853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:26.078171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:27.163819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:28.216846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:29.443827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:30.541911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:31.779842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:32.850641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:33.878664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:35.134324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:36.293855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:37.383419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:38.688169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:21.571187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:22.809548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:23.862664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:24.906615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:26.146083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:27.226357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:28.277960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:29.509730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:30.604539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:31.846022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:32.912984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:33.939639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:35.203278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:36.363575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:37.447716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:38.760238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:21.646602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:22.882544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:23.931059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:24.976744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:26.217178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:27.296001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:28.345306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:29.582110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:30.676669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:31.920443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:32.980518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:34.006623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:35.278496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:36.442015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:37.517072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:38.826203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:21.720077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:22.956293image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:23.999213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:25.046994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:26.291019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:27.365891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:28.415463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:29.658018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:30.747722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:31.994627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:33.050700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:34.077398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:35.355298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:36.518085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:37.587094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:38.892608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:21.788745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:23.023337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:24.059070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:25.111922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:26.357617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:27.428915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:28.475574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:29.724056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:30.813231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:32.064057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:33.110845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:34.137859image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:35.423431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:36.586294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T15:58:37.807798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-09-23T15:58:56.644734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
CO 1st Max HourCO 1st Max ValueCO AQICO MeanNO2 1st Max HourNO2 1st Max ValueNO2 AQINO2 MeanO3 1st Max HourO3 1st Max ValueO3 AQIO3 MeanSO2 1st Max HourSO2 1st Max ValueSO2 AQISO2 Mean
CO 1st Max Hour1.0000.1770.0540.0980.4030.2210.2210.182-0.074-0.085-0.089-0.1770.3820.1400.1400.082
CO 1st Max Value0.1771.0001.0000.9060.1070.7340.7340.812-0.229-0.454-0.469-0.6590.2780.7060.7220.547
CO AQI0.0541.0001.0000.9550.0480.7220.7220.828-0.248-0.500-0.514-0.6900.2430.7130.7260.561
CO Mean0.0980.9060.9551.0000.1070.6930.6930.826-0.240-0.491-0.501-0.6840.2530.6770.6880.550
NO2 1st Max Hour0.4030.1070.0480.1071.0000.2440.2440.1770.001-0.031-0.031-0.1670.3560.0800.0810.046
NO2 1st Max Value0.2210.7340.7220.6930.2441.0001.0000.855-0.110-0.172-0.188-0.4490.3220.6240.6380.493
NO2 AQI0.2210.7340.7220.6930.2441.0001.0000.855-0.110-0.172-0.188-0.4490.3220.6240.6380.493
NO2 Mean0.1820.8120.8280.8260.1770.8550.8551.000-0.217-0.474-0.490-0.7330.3140.7010.7120.577
O3 1st Max Hour-0.074-0.229-0.248-0.2400.001-0.110-0.110-0.2171.0000.3780.3760.357-0.121-0.181-0.179-0.165
O3 1st Max Value-0.085-0.454-0.500-0.491-0.031-0.172-0.172-0.4740.3781.0000.9940.897-0.175-0.344-0.342-0.289
O3 AQI-0.089-0.469-0.514-0.501-0.031-0.188-0.188-0.4900.3760.9941.0000.898-0.177-0.365-0.365-0.307
O3 Mean-0.177-0.659-0.690-0.684-0.167-0.449-0.449-0.7330.3570.8970.8981.000-0.290-0.525-0.528-0.429
SO2 1st Max Hour0.3820.2780.2430.2530.3560.3220.3220.314-0.121-0.175-0.177-0.2901.0000.3010.3860.194
SO2 1st Max Value0.1400.7060.7130.6770.0800.6240.6240.701-0.181-0.344-0.365-0.5250.3011.0001.0000.867
SO2 AQI0.1400.7220.7260.6880.0810.6380.6380.712-0.179-0.342-0.365-0.5280.3861.0001.0000.853
SO2 Mean0.0820.5470.5610.5500.0460.4930.4930.577-0.165-0.289-0.307-0.4290.1940.8670.8531.000

Missing values

2025-09-23T15:58:39.031989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-09-23T15:58:39.211364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-09-23T15:58:39.374663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

State CodeCounty CodeSite NumAddressStateCountyCityDate LocalNO2 UnitsNO2 MeanNO2 1st Max ValueNO2 1st Max HourNO2 AQIO3 UnitsO3 MeanO3 1st Max ValueO3 1st Max HourO3 AQISO2 UnitsSO2 MeanSO2 1st Max ValueSO2 1st Max HourSO2 AQICO UnitsCO MeanCO 1st Max ValueCO 1st Max HourCO AQI
2000-01-0141330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2000-01-01Parts per billion19.04166749.01946Parts per million0.0225000.0401034Parts per billion3.0000009.02113.0Parts per million1.1458334.221NaN
2000-01-0141330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2000-01-01Parts per billion19.04166749.01946Parts per million0.0225000.0401034Parts per billion3.0000009.02113.0Parts per million0.8789472.22325.0
2000-01-0141330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2000-01-01Parts per billion19.04166749.01946Parts per million0.0225000.0401034Parts per billion2.9750006.623NaNParts per million1.1458334.221NaN
2000-01-0141330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2000-01-01Parts per billion19.04166749.01946Parts per million0.0225000.0401034Parts per billion2.9750006.623NaNParts per million0.8789472.22325.0
2000-01-0241330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2000-01-02Parts per billion22.95833336.01934Parts per million0.0133750.0321027Parts per billion1.9583333.0224.0Parts per million0.8500001.623NaN
2000-01-0241330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2000-01-02Parts per billion22.95833336.01934Parts per million0.0133750.0321027Parts per billion1.9583333.0224.0Parts per million1.0666672.3026.0
2000-01-0241330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2000-01-02Parts per billion22.95833336.01934Parts per million0.0133750.0321027Parts per billion1.9375002.623NaNParts per million0.8500001.623NaN
2000-01-0241330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2000-01-02Parts per billion22.95833336.01934Parts per million0.0133750.0321027Parts per billion1.9375002.623NaNParts per million1.0666672.3026.0
2000-01-0341330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2000-01-03Parts per billion38.12500051.0848Parts per million0.0079580.016914Parts per billion5.2000008.320NaNParts per million1.7625002.5828.0
2000-01-0341330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2000-01-03Parts per billion38.12500051.0848Parts per million0.0079580.016914Parts per billion5.2000008.320NaNParts per million1.9291674.48NaN
State CodeCounty CodeSite NumAddressStateCountyCityDate LocalNO2 UnitsNO2 MeanNO2 1st Max ValueNO2 1st Max HourNO2 AQIO3 UnitsO3 MeanO3 1st Max ValueO3 1st Max HourO3 AQISO2 UnitsSO2 MeanSO2 1st Max ValueSO2 1st Max HourSO2 AQICO UnitsCO MeanCO 1st Max ValueCO 1st Max HourCO AQI
2015-12-2941330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2015-12-29Parts per billion25.00000046.02043Parts per million0.0160420.0371034Parts per billion1.4347834.006.0Parts per million0.6666671.3215.0
2015-12-2941330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2015-12-29Parts per billion25.00000046.02043Parts per million0.0160420.0371034Parts per billion1.5000003.02NaNParts per million0.6260871.40NaN
2015-12-3041330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2015-12-30Parts per billion28.90476250.01947Parts per million0.0138750.0361033Parts per billion1.1571432.323NaNParts per million0.6260871.621NaN
2015-12-3041330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2015-12-30Parts per billion28.90476250.01947Parts per million0.0138750.0361033Parts per billion1.1571432.323NaNParts per million0.6125000.9110.0
2015-12-3041330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2015-12-30Parts per billion28.90476250.01947Parts per million0.0138750.0361033Parts per billion1.1304353.0214.0Parts per million0.6260871.621NaN
2015-12-3041330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2015-12-30Parts per billion28.90476250.01947Parts per million0.0138750.0361033Parts per billion1.1304353.0214.0Parts per million0.6125000.9110.0
2015-12-3141330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2015-12-31Parts per billion34.12500045.0942Parts per million0.0100000.0191018Parts per billion2.0416675.087.0Parts per million0.9583331.3215.0
2015-12-3141330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2015-12-31Parts per billion34.12500045.0942Parts per million0.0100000.0191018Parts per billion2.0125004.08NaNParts per million0.9250002.08NaN
2015-12-3141330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2015-12-31Parts per billion34.12500045.0942Parts per million0.0100000.0191018Parts per billion2.0416675.087.0Parts per million0.9250002.08NaN
2015-12-3141330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2015-12-31Parts per billion34.12500045.0942Parts per million0.0100000.0191018Parts per billion2.0125004.08NaNParts per million0.9583331.3215.0